Analysis of Cut-off Points for the CAGE Questionnaire for Alcohol Abuse

Abstract

Physicians are in a key position to diagnose and treat patients with alcohol-related problems. Early interventions before the onset of these problems may decrease the costly health care as well as the psychological and social burden of alcoholism on the patient as well as the society. At this stage, the need for physicians to screen alcohol users systematically with a simple, effective and accurate instrument is becoming more critical, Being an easy-to-administer, low-cost, sensitive and specific screening tool, CAGE Questionnaire meets these criteria and offers the promise of raising the identification rate of alcoholic patients substantially. However, CAGE has still been reported to miss nearly half of risk-drinkers because of the incorrect setting of the high likelihood criterion for the presence of alcoholism, Therefore, there is a need to determine a clinically significant cut-off point above which CAGE will be diagnostic, This article aims to identify these optimal work-points for three different clinical settings by employing a step-wise application of statistical indices such as the area under the ROC curve, leveling factor and Yonden index, This method will enable health care providers to determine the optimal CAGE scores for different treatment settings and significantly decrease the number of unrecognized at-risk drinkers,

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Document Details

Document Type
Technical Report
Publication Date
Oct 25, 2001
Accession Number
ADA409707

Entities

People

  • Albert Guvenis
  • Mehmet T. Taner

Organizations

  • Boğaziçi University

Tags

DTIC Thesaurus Topics

  • Alcoholism
  • Biomedical Engineering
  • Digestive System Processes
  • Diseases And Disorders
  • Engineering
  • Factor Analysis
  • Health Care
  • Health Services
  • Leveling
  • Maximum Likelihood Estimation
  • Military Research
  • Physicians
  • Questionnaires
  • Sensitivity
  • Social Problems
  • Statistics
  • Universities

Fields of Study

  • Medicine

Readers

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  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms